






import json
import logging
import os
import re
import sys
from dataclasses import dataclass, field
from typing import Any, Dict, List, Optional, Union

import datasets
import numpy as np
import torch
import torchaudio
from packaging import version
from torch import nn

import transformers
from transformers import (
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaForCTC,
    WavaVecaProcessor,
    is_apex_available,
    set_seed,
)
from transformers.trainer_utils import get_last_checkpoint, is_main_process


if is_apex_available():
							from apex import amp


if version.parse(version.parse(torch.__version__).base_version) >= version.parse("1.6"):
							UpperCamelCase								=		True
							from torch.cuda.amp import autocast

UpperCamelCase								=		logging.getLogger(__name__)






def    __magic_name__    (	SCREAMING_SNAKE_CASE=None     ,     SCREAMING_SNAKE_CASE=None				)     ->    Dict:
							return field(default_factory=lambda: default     ,     metadata=SCREAMING_SNAKE_CASE				)

@dataclass
class 				lowerCAmelCase_	:
				_UpperCamelCase					:       str							       = field(
				    metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}			)
				_UpperCamelCase					:       Optional[str]							       = field(
				    default=__snake_case   ,		metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}   ,		)
				_UpperCamelCase					:       Optional[bool]							       = field(
				    default=__snake_case   ,		metadata={"help": "Whether to freeze the feature extractor layers of the model."}			)
				_UpperCamelCase					:       Optional[float]							       = field(
				    default=0.1   ,		metadata={"help": "The dropout ratio for the attention probabilities."}			)
				_UpperCamelCase					:       Optional[float]							       = field(
				    default=0.1   ,		metadata={"help": "The dropout ratio for activations inside the fully connected layer."}			)
				_UpperCamelCase					:       Optional[float]							       = field(
				    default=0.1   ,		metadata={
				        "help": "The dropout probabilitiy for all fully connected layers in the embeddings, encoder, and pooler."
				    }   ,		)
				_UpperCamelCase					:       Optional[float]							       = field(
				    default=0.1   ,		metadata={"help": "The dropout probabilitiy for all 1D convolutional layers in feature extractor."}   ,		)
				_UpperCamelCase					:       Optional[float]							       = field(
				    default=0.0_5   ,		metadata={
				        "help": (
				            "Propability of each feature vector along the time axis to be chosen as the start of the vector"
				            "span to be masked. Approximately ``mask_time_prob * sequence_length // mask_time_length`` feature"
				            "vectors will be masked along the time axis. This is only relevant if ``apply_spec_augment is True``."
				        )
				    }   ,		)
				_UpperCamelCase					:       Optional[float]							       = field(default=0.0   ,		metadata={"help": "The LayerDrop probability."}			)




@dataclass
class 				lowerCAmelCase_	:
				_UpperCamelCase					:       Optional[str]							       = field(
				    default=__snake_case   ,		metadata={"help": "The configuration name of the dataset to use (via the datasets library)."}			)
				_UpperCamelCase					:       Optional[str]							       = field(
				    default="train+validation"   ,		metadata={
				        "help": "The name of the training data set split to use (via the datasets library). Defaults to \'train\'"
				    }   ,		)
				_UpperCamelCase					:       bool							       = field(
				    default=__snake_case   ,		metadata={"help": "Overwrite the cached preprocessed datasets or not."}			)
				_UpperCamelCase					:       Optional[int]							       = field(
				    default=__snake_case   ,		metadata={"help": "The number of processes to use for the preprocessing."}   ,		)
				_UpperCamelCase					:       Optional[int]							       = field(
				    default=__snake_case   ,		metadata={
				        "help": (
				            "For debugging purposes or quicker training, truncate the number of training examples to this "
				            "value if set."
				        )
				    }   ,		)
				_UpperCamelCase					:       Optional[int]							       = field(
				    default=__snake_case   ,		metadata={
				        "help": (
				            "For debugging purposes or quicker training, truncate the number of validation examples to this "
				            "value if set."
				        )
				    }   ,		)
				_UpperCamelCase					:       List[str]							       = list_field(
				    default=[",", "?", ".", "!", "-", ";", ":", "\"\"", "%", "\'", "\"", "�"]   ,		metadata={"help": "A list of characters to remove from the transcripts."}   ,		)




@dataclass
class 				lowerCAmelCase_	:
				_UpperCamelCase					:       WavaVecaProcessor
				_UpperCamelCase					:       Union[bool, str]							       = True
				_UpperCamelCase					:       Optional[int]							       = None
				_UpperCamelCase					:       Optional[int]							       = None
				_UpperCamelCase					:       Optional[int]							       = None
				_UpperCamelCase					:       Optional[int]							       = None


				def __call__(					self ,						_lowerCAmelCase      ):
											_lowercase							:   List[str] 							=   [{'input_values': feature['input_values']} for feature in features]
											_lowercase							:   str 							=   [{'input_ids': feature['labels']} for feature in features]

											_lowercase							:   Union[str, Any] 							=   self.processor.pad(
											    _lowerCAmelCase ,						padding=self.padding ,						max_length=self.max_length ,						pad_to_multiple_of=self.pad_to_multiple_of ,						return_tensors='pt' ,						)
											_lowercase							:   Tuple 							=   self.processor.pad(
											    labels=_lowerCAmelCase ,						padding=self.padding ,						max_length=self.max_length_labels ,						pad_to_multiple_of=self.pad_to_multiple_of_labels ,						return_tensors='pt' ,						)

											# replace padding with -100 to ignore loss correctly
											_lowercase							:   List[Any] 							=   labels_batch['input_ids'].masked_fill(labels_batch.attention_mask.ne(1      ) ,						-1_0_0      )

											_lowercase							:   Any 							=   labels

											return batch




class 				lowerCAmelCase_	(		__snake_case			):


				def 						__a					(					self ,						_lowerCAmelCase ,						_lowerCAmelCase      ):
											model.train()
											_lowercase							:   Union[str, Any] 							=   self._prepare_inputs(_lowerCAmelCase      )

											if self.use_amp:
																		with autocast():
																									_lowercase							:   List[str] 							=   self.compute_loss(_lowerCAmelCase ,						_lowerCAmelCase      )
											else:
																		_lowercase							:   Tuple 							=   self.compute_loss(_lowerCAmelCase ,						_lowerCAmelCase      )

											if self.args.n_gpu > 1:
																		if model.module.config.ctc_loss_reduction == "mean":
																									_lowercase							:   int 							=   loss.mean()
																		elif model.module.config.ctc_loss_reduction == "sum":
																									_lowercase							:   Optional[Any] 							=   loss.sum() / (inputs['labels'] >= 0).sum()
																		else:
																									raise ValueError(F"""{model.config.ctc_loss_reduction} is not valid. Choose one of ['mean', 'sum']"""      )

											if self.args.gradient_accumulation_steps > 1:
																		_lowercase							:   Union[str, Any] 							=   loss / self.args.gradient_accumulation_steps

											if self.use_amp:
																		self.scaler.scale(_lowerCAmelCase      ).backward()
											elif self.use_apex:
																		with amp.scale_loss(_lowerCAmelCase ,						self.optimizer      ) as scaled_loss:
																									scaled_loss.backward()
											elif self.deepspeed:
																		self.deepspeed.backward(_lowerCAmelCase      )
											else:
																		loss.backward()

											return loss.detach()






def    __magic_name__    (	)     ->    Tuple:
							_lowercase							:   int 							=   HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)				)
							if len(sys.argv				) == 2 and sys.argv[1].endswith('.json'				):
														# If we pass only one argument to the script and it's the path to a json file,
														# let's parse it to get our arguments.
														_lowercase							,							_lowercase							,							_lowercase							:   Optional[Any] 							=   parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]				)				)
							else:
														_lowercase							,							_lowercase							,							_lowercase							:   int 							=   parser.parse_args_into_dataclasses()

							# Detecting last checkpoint.
							_lowercase							:   Optional[int] 							=   None
							if os.path.isdir(training_args.output_dir				) and training_args.do_train and not training_args.overwrite_output_dir:
														_lowercase							:   int 							=   get_last_checkpoint(training_args.output_dir				)
														if last_checkpoint is None and len(os.listdir(training_args.output_dir				)				) > 0:
																					raise ValueError(
																					    F"""Output directory ({training_args.output_dir}) already exists and is not empty. """
																					    'Use --overwrite_output_dir to overcome.'				)
														elif last_checkpoint is not None:
																					logger.info(
																					    F"""Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change """
																					    'the `--output_dir` or add `--overwrite_output_dir` to train from scratch.'				)

    # Setup logging
							logging.basicConfig(
							    format='%(asctime)s - %(levelname)s - %(name)s - %(message)s'     ,     datefmt='%m/%d/%Y %H:%M:%S'     ,     handlers=[logging.StreamHandler(sys.stdout				)]     ,     )
							logger.setLevel(logging.INFO if is_main_process(training_args.local_rank				) else logging.WARN				)

							# Log on each process the small summary:
							logger.warning(
							    F"""Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"""
							    + F"""distributed training: {bool(training_args.local_rank != -1				)}, 16-bits training: {training_args.fpaa}"""				)
							# Set the verbosity to info of the Transformers logger (on main process only):
							if is_main_process(training_args.local_rank				):
														transformers.utils.logging.set_verbosity_info()
							logger.info('Training/evaluation parameters %s'     ,     SCREAMING_SNAKE_CASE				)

							# Set seed before initializing model.
							set_seed(training_args.seed				)

							# Get the datasets:
							_lowercase							:   List[str] 							=   datasets.load_dataset(
							    'common_voice'     ,     data_args.dataset_config_name     ,     split=data_args.train_split_name				)
							_lowercase							:   List[str] 							=   datasets.load_dataset('common_voice'     ,     data_args.dataset_config_name     ,     split='test'				)

							# Create and save tokenizer
							_lowercase							:   List[Any] 							=   F"""[{''.join(data_args.chars_to_ignore				)}]"""

							def remove_special_characters(SCREAMING_SNAKE_CASE				):
														_lowercase							:   List[Any] 							=   re.sub(SCREAMING_SNAKE_CASE     ,     ''     ,     batch['sentence']				).lower() + ' '
														return batch

							_lowercase							:   Dict 							=   train_dataset.map(SCREAMING_SNAKE_CASE     ,     remove_columns=['sentence']				)
							_lowercase							:   int 							=   eval_dataset.map(SCREAMING_SNAKE_CASE     ,     remove_columns=['sentence']				)

							def extract_all_chars(SCREAMING_SNAKE_CASE				):
														_lowercase							:   List[str] 							=   ' '.join(batch['text']				)
														_lowercase							:   Union[str, Any] 							=   list(set(SCREAMING_SNAKE_CASE				)				)
														return {"vocab": [vocab], "all_text": [all_text]}

							_lowercase							:   int 							=   train_dataset.map(
							    SCREAMING_SNAKE_CASE     ,     batched=SCREAMING_SNAKE_CASE     ,     batch_size=-1     ,     keep_in_memory=SCREAMING_SNAKE_CASE     ,     remove_columns=train_dataset.column_names     ,     )
							_lowercase							:   Optional[int] 							=   train_dataset.map(
							    SCREAMING_SNAKE_CASE     ,     batched=SCREAMING_SNAKE_CASE     ,     batch_size=-1     ,     keep_in_memory=SCREAMING_SNAKE_CASE     ,     remove_columns=eval_dataset.column_names     ,     )

							_lowercase							:   List[str] 							=   list(set(vocab_train['vocab'][0]				) | set(vocab_test['vocab'][0]				)				)
							_lowercase							:   List[str] 							=   {v: k for k, v in enumerate(SCREAMING_SNAKE_CASE				)}
							_lowercase							:   List[Any] 							=   vocab_dict[' ']
							del vocab_dict[" "]
							_lowercase							:   int 							=   len(SCREAMING_SNAKE_CASE				)
							_lowercase							:   Optional[Any] 							=   len(SCREAMING_SNAKE_CASE				)

							with open('vocab.json'     ,     'w'				) as vocab_file:
														json.dump(SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE				)

							# Load pretrained model and tokenizer
							#
							# Distributed training:
							# The .from_pretrained methods guarantee that only one local process can concurrently
							# download model & vocab.
							_lowercase							:   Optional[Any] 							=   WavaVecaCTCTokenizer(
							    'vocab.json'     ,     unk_token='[UNK]'     ,     pad_token='[PAD]'     ,     word_delimiter_token='|'     ,     )
							_lowercase							:   List[str] 							=   WavaVecaFeatureExtractor(
							    feature_size=1     ,     sampling_rate=16_000     ,     padding_value=0.0     ,     do_normalize=SCREAMING_SNAKE_CASE     ,     return_attention_mask=SCREAMING_SNAKE_CASE				)
							_lowercase							:   List[Any] 							=   WavaVecaProcessor(feature_extractor=SCREAMING_SNAKE_CASE     ,     tokenizer=SCREAMING_SNAKE_CASE				)
							_lowercase							:   List[str] 							=   WavaVecaForCTC.from_pretrained(
							    model_args.model_name_or_path     ,     cache_dir=model_args.cache_dir     ,     activation_dropout=model_args.activation_dropout     ,     attention_dropout=model_args.attention_dropout     ,     hidden_dropout=model_args.hidden_dropout     ,     feat_proj_dropout=model_args.feat_proj_dropout     ,     mask_time_prob=model_args.mask_time_prob     ,     gradient_checkpointing=training_args.gradient_checkpointing     ,     layerdrop=model_args.layerdrop     ,     ctc_loss_reduction='mean'     ,     pad_token_id=processor.tokenizer.pad_token_id     ,     vocab_size=len(processor.tokenizer				)     ,     )

							if data_args.max_train_samples is not None:
														_lowercase							:   List[str] 							=   min(len(SCREAMING_SNAKE_CASE				)     ,     data_args.max_train_samples				)
														_lowercase							:   List[str] 							=   train_dataset.select(range(SCREAMING_SNAKE_CASE				)				)

							if data_args.max_val_samples is not None:
														_lowercase							:   List[str] 							=   eval_dataset.select(range(data_args.max_val_samples				)				)

							_lowercase							:   Union[str, Any] 							=   torchaudio.transforms.Resample(48_000     ,     16_000				)

							# Preprocessing the datasets.
							# We need to read the aduio files as arrays and tokenize the targets.
							def speech_file_to_array_fn(SCREAMING_SNAKE_CASE				):
														_lowercase							,							_lowercase							:   str 							=   torchaudio.load(batch['path']				)
														_lowercase							:   Any 							=   resampler(SCREAMING_SNAKE_CASE				).squeeze().numpy()
														_lowercase							:   Union[str, Any] 							=   16_000
														_lowercase							:   Optional[Any] 							=   batch['text']
														return batch

							_lowercase							:   Optional[int] 							=   train_dataset.map(
							    SCREAMING_SNAKE_CASE     ,     remove_columns=train_dataset.column_names     ,     num_proc=data_args.preprocessing_num_workers     ,     )
							_lowercase							:   Tuple 							=   eval_dataset.map(
							    SCREAMING_SNAKE_CASE     ,     remove_columns=eval_dataset.column_names     ,     num_proc=data_args.preprocessing_num_workers     ,     )

							def prepare_dataset(SCREAMING_SNAKE_CASE				):
														# check that all files have the correct sampling rate
														assert (
														    len(set(batch['sampling_rate']				)				) == 1
														), F"""Make sure all inputs have the same sampling rate of {processor.feature_extractor.sampling_rate}."""

														_lowercase							:   Optional[Any] 							=   processor(
														    audio=batch['speech']     ,     text=batch['target_text']     ,     sampling_rate=batch['sampling_rate'][0]				)
														batch.update(SCREAMING_SNAKE_CASE				)
														return batch

							_lowercase							:   List[Any] 							=   train_dataset.map(
							    SCREAMING_SNAKE_CASE     ,     remove_columns=train_dataset.column_names     ,     batch_size=training_args.per_device_train_batch_size     ,     batched=SCREAMING_SNAKE_CASE     ,     num_proc=data_args.preprocessing_num_workers     ,     )
							_lowercase							:   Optional[Any] 							=   eval_dataset.map(
							    SCREAMING_SNAKE_CASE     ,     remove_columns=eval_dataset.column_names     ,     batch_size=training_args.per_device_train_batch_size     ,     batched=SCREAMING_SNAKE_CASE     ,     num_proc=data_args.preprocessing_num_workers     ,     )

							# Metric
							_lowercase							:   Any 							=   datasets.load_metric('wer'				)

							def compute_metrics(SCREAMING_SNAKE_CASE				):
														_lowercase							:   str 							=   pred.predictions
														_lowercase							:   int 							=   np.argmax(SCREAMING_SNAKE_CASE     ,     axis=-1				)

														_lowercase							:   Union[str, Any] 							=   processor.tokenizer.pad_token_id

														_lowercase							:   List[str] 							=   processor.batch_decode(SCREAMING_SNAKE_CASE				)
														# we do not want to group tokens when computing the metrics
														_lowercase							:   List[str] 							=   processor.batch_decode(pred.label_ids     ,     group_tokens=SCREAMING_SNAKE_CASE				)

														_lowercase							:   Any 							=   wer_metric.compute(predictions=SCREAMING_SNAKE_CASE     ,     references=SCREAMING_SNAKE_CASE				)

														return {"wer": wer}

							if model_args.freeze_feature_extractor:
														model.freeze_feature_extractor()

							# Data collator
							_lowercase							:   Optional[int] 							=   DataCollatorCTCWithPadding(processor=SCREAMING_SNAKE_CASE     ,     padding=SCREAMING_SNAKE_CASE				)

							# Initialize our Trainer
							_lowercase							:   Any 							=   CTCTrainer(
							    model=SCREAMING_SNAKE_CASE     ,     data_collator=SCREAMING_SNAKE_CASE     ,     args=SCREAMING_SNAKE_CASE     ,     compute_metrics=SCREAMING_SNAKE_CASE     ,     train_dataset=train_dataset if training_args.do_train else None     ,     eval_dataset=eval_dataset if training_args.do_eval else None     ,     tokenizer=processor.feature_extractor     ,     )

							# Training
							if training_args.do_train:
														if last_checkpoint is not None:
																					_lowercase							:   Tuple 							=   last_checkpoint
														elif os.path.isdir(model_args.model_name_or_path				):
																					_lowercase							:   Any 							=   model_args.model_name_or_path
														else:
																					_lowercase							:   int 							=   None

														# Save the feature_extractor and the tokenizer
														if is_main_process(training_args.local_rank				):
																					processor.save_pretrained(training_args.output_dir				)

														_lowercase							:   Optional[int] 							=   trainer.train(resume_from_checkpoint=SCREAMING_SNAKE_CASE				)
														trainer.save_model()

														_lowercase							:   str 							=   train_result.metrics
														_lowercase							:   List[Any] 							=   (
														    data_args.max_train_samples if data_args.max_train_samples is not None else len(SCREAMING_SNAKE_CASE				)
														)
														_lowercase							:   int 							=   min(SCREAMING_SNAKE_CASE     ,     len(SCREAMING_SNAKE_CASE				)				)

														trainer.log_metrics('train'     ,     SCREAMING_SNAKE_CASE				)
														trainer.save_metrics('train'     ,     SCREAMING_SNAKE_CASE				)
														trainer.save_state()

							# Evaluation
							_lowercase							:   str 							=   {}
							if training_args.do_eval:
														logger.info('*** Evaluate ***'				)
														_lowercase							:   int 							=   trainer.evaluate()
														_lowercase							:   Any 							=   data_args.max_val_samples if data_args.max_val_samples is not None else len(SCREAMING_SNAKE_CASE				)
														_lowercase							:   Optional[int] 							=   min(SCREAMING_SNAKE_CASE     ,     len(SCREAMING_SNAKE_CASE				)				)

														trainer.log_metrics('eval'     ,     SCREAMING_SNAKE_CASE				)
														trainer.save_metrics('eval'     ,     SCREAMING_SNAKE_CASE				)

							return results


if __name__ == "__main__":
							main()










'''simple docstring'''







def 						_A      ( UpperCAmelCase						):






   '''simple docstring'''







   A__       =     0
   while num > 0:
      digit_sum += num % 10
      num //= 10
   return digit_sum







def 						_A      ( UpperCAmelCase = 100						):






   '''simple docstring'''







   A__       =     1
   A__       =     2

   for i in range(2      ,max_n + 1						):
      A__       =     pre_numerator
      A__       =     2 * i // 3 if i % 3 == 0 else 1
      A__       =     cur_numerator
      A__       =     e_cont * pre_numerator + temp

   return sum_digits(UpperCAmelCase						)


if __name__ == "__main__":
    print(f'''{solution() = }''')


